As an alternative, you can always use the ggplot2 library. Because of the way the data is shaped, you should also use the reshape2 library to differentiate between variables. It's a bit more complicated in this case, but in general you'll get nicer-looking barplots.

library(ggplot2)
library(reshape2)
#id variable tells what row number is used
data1=as.data.frame(cbind(id=1:4,var1=c(10,20,50,100),var2=c(75,80,85,92)))
#melt will create a row for each variable of each row, except it saves the id as a separate variable that's on every row
data1=melt(data1,id.vars='id')
#ggplot tells what data set is used and which variables do what
#geom_bar tells what type of plot should be used and certain options for the plot
ggplot(data1,aes(x=id,y=value,fill=variable))+geom_bar(stat='identity',position='dodge')